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Xiaodong Huang
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

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Journal article
Published: 07 November 2019 in Sustainability
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Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.

ACS Style

Xiaodong Huang; Wenkai Liu; Yuping Han; Chunying Wang; Han Wang; Sai Hu. Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites. Sustainability 2019, 11, 6227 .

AMA Style

Xiaodong Huang, Wenkai Liu, Yuping Han, Chunying Wang, Han Wang, Sai Hu. Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites. Sustainability. 2019; 11 (22):6227.

Chicago/Turabian Style

Xiaodong Huang; Wenkai Liu; Yuping Han; Chunying Wang; Han Wang; Sai Hu. 2019. "Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites." Sustainability 11, no. 22: 6227.

Journal article
Published: 29 August 2019 in Water
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It is generally acknowledged that soil erosion has become one of the greatest global threats to the human–environment system. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely used for soil erosion estimation, the algorithm for calculating soil erodibility factor (K) in this equation remains limited, particularly in the context of China, which features highly diverse soil types. In order to address the problem, a modified algorithm describing the piecewise function of gravel content and relative soil erosion was used for the first time to modify the soil erodibility factor, because it has been proven that gravel content has an important effect on soil erosion. The Chaohu Lake Basin (CLB) in East China was used as an example to assess whether our proposal can improve the accuracy of soil erodibility calculation and soil erosion estimation compared with measured data. Results show that (1) taking gravel content into account helps to improve the calculation of soil erodibility and soil erosion estimation due to its protection to topsoil; (2) the overall soil erosion in the CLB was low (1.78 Mg·ha−1·year−1) the majority of which was slight erosion (accounting for 85.6%) and no extremely severe erosion; and (3) inappropriate land use such as steep slope reclamation and excessive vegetation destruction are the main reasons for soil erosion of the CLB. Our study will contribute to decision-makers to develop soil and water conservation policies.

ACS Style

Sai Hu; Long Li; Longqian Chen; Liang Cheng; Lina Yuan; Xiaodong Huang; Ting Zhang. Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model. Water 2019, 11, 1806 .

AMA Style

Sai Hu, Long Li, Longqian Chen, Liang Cheng, Lina Yuan, Xiaodong Huang, Ting Zhang. Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model. Water. 2019; 11 (9):1806.

Chicago/Turabian Style

Sai Hu; Long Li; Longqian Chen; Liang Cheng; Lina Yuan; Xiaodong Huang; Ting Zhang. 2019. "Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model." Water 11, no. 9: 1806.